Benchmarking CUT&RUN analysis procedure using motif enrichment

To support this work, we generated new CUT&RUN datasets with distinct experimental characteristics alongside publicly available datasets. In particular, we produced libraries with an average read length of approximately 133 bp—longer than typical CUT&RUN read lengths—which provide higher sequencing coverage and improved read mappability.

These datasets provide complementary data for evaluating how experimental characteristics influence preprocessing strategies and peak-calling performance in CUT&RUN.